Papers - Takahashi, Hiroshi

A Study on the Influence of Managerial Appearance on Corporate Behavior

FUKASAWA Ayumi, TAKAHASHI Hiroshi

JSAI Technical Report, Type 2 SIG (The Japanese Society for Artificial Intelligence)  2020 ( BI-016 ) 04 2020.08

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<p>In Europe and the United States, appearance is considered to be an important factor in business,and not a few studies have examined the effects of managerial appearance on corporate management andindividual success and performance. In this study, we attempt to analyze quantitatively the faces ofmanagers in Japan, which is one of the most important components of appearance, in order to elucidatewhether a similar phenomenon is found in Japan. Photographs of managers' faces are collected fromannual reports and other sources, and feature vectors are extracted. This will be clustered using theK-means method using FaceNet's trained model VGGFace2 to analyze whether there are differences inthe behavior of the companies in each cluster, i.e., how the face of the manager affects the company'sbehavior.</p>

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Network effects and the appointment of female board members in Japan

Matthias Raddant, Hiroshi Takahashi

 2020.07

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We investigate the dynamics in the networks of Japanese corporates and its
interplay with the appointments of female board members. We find that corporate
boards with women show homophily with respect to gender while the respective
firms often have above average profitability. We also find that new
appointments of women are more likely at boards which observe female board
members at other firms to which they are tied by either ownership relations or
corporate board interlocks.


Network effects and the appointment of female board members in Japan

Matthias Raddant, Hiroshi Takahashi

 2020.07

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We investigate the dynamics in the networks of Japanese corporates and its
interplay with the appointment of female board members. We find that firms with
female board members show homophily with respect to gender and often have above
average profitability. We also find that new appointments of women are more
likely at boards which observe female board members at other firms to which
they are tied by either ownership relations or corporate board interlocks.


Interdependencies of female board member appointments

Matthias Raddant, Hiroshi Takahashi

 2020.07

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We investigate the networks of Japanese corporate boards and its influence on
the appointments of female board members. We find that corporate boards with
women show homophily with respect to gender. The corresponding firms often have
above average profitability. We also find that new appointments of women are
more likely at boards which observe female board members at other firms to
which they are tied by either ownership relations or corporate board
interlocks.


Interdependencies of female board member appointments

Matthias Raddant, Hiroshi Takahashi

 2020.07

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We investigate the networks of Japanese corporate boards and its influence on
the appointments of female board members. We find that corporate boards with
women show homophily with respect to gender. The corresponding firms often have
above average profitability. We also find that new appointments of women are
more likely at boards which observe female board members at other firms to
which they are tied by either ownership relations or corporate board
interlocks.


Untitled

NISHI Yoshihiro, SUGE Aiko, TAKAHASHI Hiroshi

JSAI Technical Report, Type 2 SIG (The Japanese Society for Artificial Intelligence)  2020 ( BI-015 ) 03 2020.03

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<p>News articles have great impacts on asset prices in the financial markets. Many attempts havebeen reported to ascertain how news influences stock prices. However, the limitations in the number ofavailable data sets usually become the hurdle for the model accuracy. In this study, we propose a newsevaluation model utilizing GPT-2. A news evaluation model is a model that evaluates news articlesdistributed to financial markets based on price fluctuation rates and predicts fluctuations in stock prices.Reuter's news texts are classified based on each return through LSTM models. Using co-occurrencenetwork analysis, we reviewed the overview of the news articles retrieved. News articles generated by GPT-2 was used with original news articles, and the model accuracy was examined. The results showed thatcreated news articles are influential over the prediction of stock price fluctuation.</p>

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Constructing valuation system through patent analysis -Evaluation of IPO offering price using patent document vectors-

FUJIWARA Shohei, MATSUMOTO Yusuke, SUGE Aiko, TAKAHASHI Hiroshi

JSAI Technical Report, Type 2 SIG (The Japanese Society for Artificial Intelligence)  2020 ( BI-015 ) 09 2020.03

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<p>Corporate valuation is an important process for determining corporate value for managersconsidering initial public offering (hereinafter IPO). However, in the IPO, the underprice phenomenonwhere the public price falls below the initial value has occurred. Due to this phenomenon, new publiccompanies suffer opportunity loss. In this study, we consider the cause of this phenomenon to be in theevaluation of corporate value and try to improve it by using patents in existing methods. Specifically,SCDVs are created based on patent documents held by companies and applied to similar companycomparison guns to evaluate the validity of the open price.</p>

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Relationship between tweet information and small-cap stocks

KATAYAMA Shinya, TAKAHASHI Hiroshi

JSAI Technical Report, Type 2 SIG (The Japanese Society for Artificial Intelligence)  2020 ( BI-015 ) 16 2020.03

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<p>本研究では、日本の株式市場における Twitter のツイート情報と小型株の株価との関連性について分析を行った。機関投資家と個人投資家の投資手法の違いを踏まえた上で、GoogleCloud Natural Language API を用いた感情分析を行い、ツイートと小型株の株価の関係を明らかにする。</p>

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Analyzing accumulation data of a restaurant

SEO Hwangsaeng, ZHANG Ying, SUGE Aiko, TAKAHASHI Hiroshi

JSAI Technical Report, Type 2 SIG (The Japanese Society for Artificial Intelligence)  2020 ( BI-015 ) 14 2020.03

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<p>本研究は、飲食店のPOSデータと顧客データを用いて企業の経営力向上に繋げるシステムの提供を目的としている。昨今のビッグデータ活用時代において、個人のデータを蓄積し利活用することの重要性が増してきている。個人が頻繁に利用しデータ取得可能な業態として飲食店が挙げられ、そこで蓄積されたデータを分析することでCRMに有用な示唆を得ることができると考える。本分析では、顧客の購買行動の特徴を明らかにし、在庫管理と販売政策について検討を行った。</p>

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The information security accident about the impact and relevance to the corporate value

MIYAUCHI Sachiko, TAKAHASHI Hiroshi

JSAI Technical Report, Type 2 SIG (The Japanese Society for Artificial Intelligence)  2020 ( BI-015 ) 12 2020.03

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<p>Information security incidents could be one of the most concerns for corporate activities.Considering these factors, this study attempts to clarify the relationship between information securityincidents and stock market reactions. In this analysis, we analyze the stock market in Japan from March2005 to January 2015. As a result of intensive analyses, we confirm the following results; (1) informationsecurity incidents have a negative impact on corporate values, (2) information security incidents withsignificant impact have long-term negative effects on stock market.</p>

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Relationship between tweet information and news articles

KATAYAMA Shinya, NISHI Yoshihiro, SUGE Aiko, TAKAHASHI Hiroshi

JSAI Technical Report, Type 2 SIG (The Japanese Society for Artificial Intelligence)  2020 ( BI-014 ) 05 2020.01

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<p>SNS が世の中に与える影響が拡大しており、特にインフルエンサーが人々に与える影響が増してきている。本研究では、株のインフルエンサーを特定した上で、Google Cloud NaturalLanguage API を用いた感情分析を行い、ツイートとニュース及び株価の関係を明らかにすることを試みる。</p>

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Analyzing the factors that determine the labor share of listed companies in Japan

MATSUMOTO Akihiro, SUGE Aiko, TAKAHASHI Hiroshi

JSAI Technical Report, Type 2 SIG (The Japanese Society for Artificial Intelligence)  2020 ( BI-014 ) 02 2020.01

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<p>In recent years, the labor share has been declining in Japan, causing a hollowing out of the realeconomy. The purpose of this study is to clarify the relationship between labor shares and payout ratios. Weplan to examine whether the relationship between the labor share and payout ratio can be demonstrated byusing nonlinear analysis such as machine learning methods.</p>

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Knowledge Graph construction using large-scale news text

ZHANG Ying, SUGE Aiko, TAKAHASHI Hiroshi

JSAI Technical Report, Type 2 SIG (The Japanese Society for Artificial Intelligence)  2020 ( BI-014 ) 11 2020.01

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<p>Investors refer to a variety of information when making investment decisions: The amount ofavailable information is increasing day by day due to the spread of the Internet, and it is difficult to graspall information such as causality. In this study, we attempt to construct an understanding support model thatcan visualize news through Attention-Based Bi-LSTM model using Reuters News as an analysis target.</p>

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Analysis of Stock Price Fluctuations by Deep Learning Using News and High-Frequency Data: BERT News Evaluation

NISHI Yoshihiro, SUGE Aiko, TAKAHASHI Hiroshi

JSAI Technical Report, Type 2 SIG (The Japanese Society for Artificial Intelligence)  2020 ( BI-014 ) 04 2020.01

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<p>ニュースは金融市場の資産価格に大きな影響を与える．ニュースと株価変動の関係性を分析し，ニュースを評価する取り組みはこれまでに多く行われており，ニュースと株価変動の間には関連性があると報告されている．しかしながら，ニュースは非構造化データであり，定型的に精度高く処理し，分析に用いることは難しい．本研究では，多くの自然言語読解タスクでSOTA(State of the Art)を達成している BERT を用いて株式変動を説明するニュース評価モデルを構築し，実証分析を行い，従来の分類モデルよりも高い正答率を得られるか検証を行った．本研究における分析の結果，BERT を用いたニュース評価モデルの精度が最も高かった．</p>

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Proposal of human resources employment strategy of labor market in food service based on agent-based model

SEO Hwangsaeng, SUGE Aiko, TAKAHASHI Hiroshi

JSAI Technical Report, Type 2 SIG (The Japanese Society for Artificial Intelligence)  2020 ( BI-014 ) 07 2020.01

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<p>Japan is suffering from a serious labor shortage due to the economic expansion anddemographic change in the non-regular employment of the labor market. In particular, non-regularemployees account for 80% of employees in the food-service industry is a critical issue. The purpose ofthe present study is to analyze a mechanism of job matching focusing on non-regular employmentthrough agent-based modeling.</p>

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Evaluation of IPO offering price using patent document vectors

FUJIWARA Shohei, MATSUMOTO Yusuke, SUGE Aiko, TAKAHASHI Hiroshi

JSAI Technical Report, Type 2 SIG (The Japanese Society for Artificial Intelligence)  2020 ( BI-014 ) 09 2020.01

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<p>There are many previous studies on IPO open prices. In particular, underpricing,where the public price is lower than the initial price, is an important issue from themanagement's point of view, as it causes loss of opportunity for issuers. Various hypothesesabout the cause of underpricing have been made by previous research. In this research, wefocus on the calculation of corporate value when determining the open price. Specifically,SCDV was created based on patent documents owned by the company. Next, a similarcompany comparison method was performed by selecting similar companies based on thecreated document vectors, and the validity of the open price was attempted.</p>

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News Articles Evaluation Analysis in Automotive Industry Using GPT-2 and Co-occurrence Network

Nishi Y, Suge A, Takahashi H

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))  12331 LNAI   103 - 114 2020

Joint Work,  ISSN  9783030587895

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News articles have great impacts on asset prices in the financial markets. Many attempts have been reported to ascertain how news influences stock prices. Stock price fluctuations of highly influential companies can have a major impact on the economy as a whole. In particular, the automobile industry is a colossal industry that leads the Japanese industry. However, the limitations in the number of available data sets usually become the hurdle for the model accuracy. In this study, we constructed a news evaluation model utilizing GPT-2. A news evaluation model is a model that evaluates news articles distributed to financial markets based on price fluctuation rates and predicts fluctuations in stock prices. We have added news articles generated by GPT-2 as data for analysis. Besides, we used a co-occurrence network analysis to review the overview of the news articles. News articles were classified through Long Short-Term Memory (LSTM). The results showed that the accuracy of the news evaluation model improved by generating news articles using a language generation model through GPT-2. More detailed analyses are planned for the future.

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Classification of Simulation Results for Formal Description of Business Case

KIKUCHI Takamasa, KUNIGAMI Masaaki, TAKAHASHI Hiroshi, TORIYAMA Masahiro, TERANO Takao

Journal of the Japan Society for Management Information (The Japan Society for Managemant Information)  29 ( 3 ) 199 - 214 2020

Joint Work,  ISSN  0918-7324

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<p>An approach in organizational research compares actual business cases with the logs of organizational simulation. In this approach, to compare business cases and simulation logs efficiently, an analysis method that targets only the types of micro-logs, and not micro-logs that indicate detailed records of individual agent behavior, has been proposed. However, the method is dependent on the analyst's knowledge of the simulation model. Consequently, a room for improvement prevails in relation to the need to set log types in advance. Therefore, in this paper, a methodology that extends the following two points by employing the clustering method to micro-log analysis of the organizational simulation is proposed. First, the analyst does not have to set the type of simulation log in advance. Second, the analyst could change the analysis granularity of the simulation log. Further, we constructed an agent-based model that represents the decision-making process related to the external environment recognition of an organization and illustrated that the requirements of the proposed method are satisfied.</p>

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Corporate value evaluation using patent document vector

FUJIWARA Shohei, MATSUMOTO Yusuke, SUGE Aiko, TAKAHASHI Hiroshi

Proceedings of the Annual Conference of JSAI (The Japanese Society for Artificial Intelligence)  2020 ( 0 ) 3Rin456 - 3Rin456 2020

Joint Work

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<p>Corporate valuation is one of the important financial modeling in IPO and M & A. In particular, at the time of IPO, there is no underlying stock price information, so its price depends on the result of corporate valuation. However, the offer price at the time of the IPO often has an under-pricing in which the public price falls below the initial value, resulting in an opportunity loss for the issuer company. Although many previous studies have referred to this issue, no consensus has yet been obtained. In this study, we consider that there is room for improvement in the corporate value evaluation model and try to evaluate the corporate value by reproducing the initial value of a newly listed company using patent information. Specifically, first, Derwent World Patents Index held by the company is extracted, and the patent document is vectorized by using Sparse Composite Document Vector. Next, the center of gravity of the patent is calculated from the vectorized patent document, and the similarity between the companies is expressed by the distance to select similar companies. Finally, using the selected companies, a comparable peer company multiple method is performed to evaluate the validity of the stock price. In conclusion, it is suggested that the model is more explanatory than using industry average financial figures. In this study, we analyzed feasibility of our proposed method.</p>

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Construction and Analysis of News Evaluation Model for Electric Manufacturers Using Natural Language Generation through GPT-2

NISHI Yoshihiro, SUGE Aiko, TAKAHASHI Hiroshi

Proceedings of the Annual Conference of JSAI (The Japanese Society for Artificial Intelligence)  2020 ( 0 ) 4Rin172 - 4Rin172 2020

Joint Work,  ISSN  2758-7347

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<p>News articles distributed in financial markets are essential information that affects asset valuation. Many studies have analyzed the impact of delivered news articles on stock price fluctuations. However, the number of news articles is limited, and the limit on the number of data that can be obtained affects the accuracy of analysis using deep learning. In this study, we constructed a news evaluation model based on stock price fluctuation before and after news article distribution and tried to improve the accuracy of the model by generating news articles through GPT-2. The analysis target was a Japanese electronics manufacturer, and analysis was performed using a news evaluation model. As a result of the analysis, we found that there is a possibility that the generation of news articles through GPT-2 can improve the accuracy of the news evaluation model.</p>

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